Systems and methods for analyzing machine performance

ABSTRACT

Methods, systems, and devices for analyzing vibration data to determine and mitigate an occurrence of an anomaly at an industrial asset. A device may obtain and preprocess vibration data, which may include grouping the vibration data according to respective time intervals, and the device may filter the grouped data to obtain the most recent measurement readings. The device may use an anomaly detection algorithm to detect outliers in the datasets for each group of vibration data and compare an amplitude of the outliers to a threshold for the respective group. The device may determine that the vibration data includes an anomaly based on quantities of outliers in first and second subsets of the vibration data that are within a detection window. The device may generate a report including an indication of the anomaly and transmit the report to an operator or engineer for the facility.

RELATED APPLICATIONS

This application claims priority to U.S. Patent Application No.63/093,137, filed Oct. 16, 2020, which is incorporated herein in itsentirety by reference.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods foranalyzing performance of industrial equipment and, more particularly, tosystems and methods for analyzing vibration data to determine andmitigate an occurrence of an anomaly at an industrial asset.

DESCRIPTION OF THE RELATED TECHNOLOGY

In some cases, an industrial mill or factory may include numerousmachines and other equipment. During operation, this equipment mayvibrate, which may indicate that the equipment has, over time, incurredwear and tear or other damage (e.g., potentially causing failures,etc.). If this goes undetected, the wear and tear or other damage maycause major issues that may then lead to unplanned events, such as ashutdown or other significant operational events. Such unplanned eventsare undesirable because they may lead to time and/or monetary losses foran owner and/or operator of the equipment. Accordingly, techniques havebeen implemented to monitor such vibrations to attempt to control forand/or prevent excessive degradation of equipment so as to avoid thesepotentially significant unplanned events. As such, many industrialmachines include sensors that measure vibration and other physicalphenomena.

Some techniques for monitoring these vibration measurements may include,for example, a system that detects when a sensor reports a vibrationmetric that satisfies or exceeds a given threshold. However, a givenindustrial machine may include hundreds of sensors, and a facility mayinclude hundreds of industrial machines. Furthermore, because vibrationsensors are subject to a variety of factors that may compromise dataintegrity, a suitable solution for normalizing vibration data collectedfrom sensors may be desirable. For example, previous solutions may notproperly control for noise, time series discrepancies, breakage events,and other factors that influence data analyses.

Moreover, some machines may, at certain times (e.g., periodically orwhen under high load), substantially increase their operations which maycorrespondingly generate an increase of a level of vibrationmeasurements. Some techniques for monitoring vibration measurements mayflag such a sudden jump in the vibration measurements in this situationas an anomaly or as potentially problematic, but this sudden jump in thevibration measurements may not be indicative of an anomalous orexcessive amount of vibrations, and thus reporting this jump may resultin a false positive.

Accordingly, the inability to control such factors may render analysesinfeasible or inaccurate. In particular, the inability to adequatelyprocess vibration data to control for variations may add significantcomplexity to performing industrial scale assessments of machines in afacility (e.g., because comparisons between given data points may berendered useless by uncontrolled disparities between the given datapoints). Thus, techniques are described herein by which operational datamay be used to detect potential issues for industrial equipment before afailure or other potentially substantial negative results occur due tothe issues (e.g., before excessive degradation occurs, which may in turncause a catastrophic failure, a temporary shutdown, or lead to theequipment needing to be prematurely replaced).

SUMMARY

The described techniques relate to improved methods, systems, devices,and apparatuses that support analyzing machine performance, such asvibration data, to determine and mitigate an occurrence of an anomaly atan industrial asset. Generally, the described techniques provide for adevice such as a server or other computing device to obtain operationaldata (e.g., vibration data), such as from one or more sensors located onor near one or more machines. For example, the sensors may be configuredto collect measurements associated with the machines in a facility(e.g., mill equipment or other machinery in an industrial facility), andthe sensors may provide this information (e.g., operational data,including vibration data) to the device. The device may preprocess theobtained vibration data, where preprocessing the data may includeconverting the vibration data into a format in which data can be groupedaccording to particular indicator types and grouping the vibration datainto one or more groups of vibration data, where each group of vibrationdata corresponds to a respective time interval.

The device may model the vibration data to identify an anomaly andassociate the anomaly with a corresponding sensor and/or machine thatmay be experiencing an issue. More specifically, modeling the vibrationdata may include filtering a dataset of operational data to obtain oneor more groups of data including vibration measurements obtained fromrecently performed measurement readings. Modeling the vibration data mayfurther include using an anomaly detection algorithm to detect outliersin the datasets for each of the respective groups of vibration data and,further, comparing an amplitude of each of the outliers to a thresholdfor the respective group of vibration data to determine a subset of theoutliers with the more excessive overall vibration readings.

The device may determine that the vibration data includes an anomalyassociated with one or more sensors based on a quantity of outliers ofthe first subset that are within a detection window relative to aquantity of outliers of the second subset that are within the detectionwindow. In some examples, the device may generate a report including anindication of this anomaly and communicate the report with an operatoror engineer for the associated facility. Based on the report, theoperator or engineer may confirm whether the device(s) associated withany indicated anomalies are indeed operating abnormally and, if needed,troubleshoot these device(s) or otherwise mitigate potential negativeeffects of the anomaly.

A method for analyzing machine performance is described. The method mayinclude receiving vibration data points from one or more sensorsassociated with one or more machines; grouping the vibration data pointsinto a first group of vibration data and a second group of vibrationdata, where the first group of vibration data includes a first set ofthe vibration data points associated with a short-term window and thesecond group of vibration data includes a second set of the vibrationdata points associated with a long-term window, each of the first groupof vibration data and the second group of vibration data correspondingto one of a plurality of time intervals; detecting one or more outliersfrom the first set of vibration data points and from the second set ofvibration data points; determining a first subset of the one or moreoutliers based on an amplitude of each outlier of the first subsetsatisfying a first threshold; determining a second subset of the one ormore outliers based on an amplitude of each outlier of the second subsetsatisfying a second threshold; determining that the vibration datapoints include an anomaly associated with one or more respective sensorsof the one or more sensors based on a quantity of outliers of the firstsubset that are within a detection window relative to a quantity ofoutliers of the second subset that are within the detection window; andtransmitting an indication of the one or more respective sensorsassociated with the anomaly.

An apparatus for analyzing machine performance is described. Theapparatus may include a processor; a memory coupled with the processor;and instructions stored in the memory and executable by the processor tocause the apparatus to receive vibration data points from one or moresensors associated with one or more machines; group the vibration datapoints into a first group of vibration data and a second group ofvibration data, where the first group of vibration data includes a firstset of the vibration data points associated with a short-term window andthe second group of vibration data includes a second set of thevibration data points associated with a long-term window, each of thefirst group of vibration data and the second group of vibration datacorresponding to one of a plurality of time intervals; detect one ormore outliers from the first set of vibration data points and from thesecond set of vibration data points; determine a first subset of the oneor more outliers based on an amplitude of each outlier of the firstsubset satisfying a first threshold; determine a second subset of theone or more outliers based on an amplitude of each outlier of the secondsubset satisfying a second threshold; determine that the vibration datapoints include an anomaly associated with one or more respective sensorsof the one or more sensors based on a quantity of outliers of the firstsubset that are within a detection window relative to a quantity ofoutliers of the second subset that are within the detection window; andtransmit an indication of the one or more respective sensors associatedwith the anomaly.

Some examples of the methods and apparatuses described herein mayfurther include operations and features for associating a plurality ofindicators with each of the vibration data points, each indicator of theplurality of indicators being common to at least a subset of the one ormore sensors, where detecting the one or more outliers is based on oneor more of the plurality of indicators, respectively, for the firstgroup of vibration data, the second group of vibration data, or both. Insome examples of the methods and apparatuses described herein, theplurality of indicators includes one or more of: a sensor type, acorresponding machine, a corresponding facility, an indicator type, adate-time, a sensor measurement, or a combination thereof. In someexamples of the methods and apparatuses described herein, the vibrationdata points include one or more of: a peak velocity value, a peakacceleration value, an overall root mean square value, an overallvibration value, or a combination thereof.

In some examples of the methods and apparatuses described herein,grouping the vibration data into the one or more groups of vibrationdata includes normalizing the vibration data of the first group ofvibration data and the second group of vibration data.

In some examples of the methods and apparatuses described herein, eachtime interval of the plurality of time intervals corresponds to arespective hour interval.

Some examples of the methods and apparatuses described herein mayfurther include operations and features for modifying the first set ofvibration data points, the second set of vibration data points, or both,based on one or more performance parameters, where grouping thevibration data points includes filtering the vibration data points basedon the modified data points. In some examples of the methods andapparatuses described herein, the one or more performance parametersinclude one or more of: a processed sensor measurement, a date-timeindicator, an indicator type, a window size parameter, a number ofprocessors, a sensitivity parameter, or a combination thereof.

In some examples of the methods and apparatuses described herein,detecting the one or more outliers include applying an isolationalgorithm to the first set of vibration data points of the first groupof vibration data and to the second set of vibration data points of thesecond group of vibration data. In some examples of the methods andapparatuses described herein, determining that the vibration data pointsinclude the anomaly is based on a variation between the quantity ofoutliers of the first subset that are within the detection window andthe quantity of outliers of the second subset that are within thedetection window. In some examples of the methods and apparatusesdescribed herein, the first threshold is set according to a thresholdpercentile for the first set of vibration data points of the short-termwindow and the second threshold is set according to the thresholdpercentile for the second set of vibration data points of the long-termwindow.

Some examples of the methods and apparatuses described herein mayfurther include operations and features for collecting the vibrationdata points at discrete intervals. Some examples of the methods andapparatuses described herein may further include operations and featuresfor associating the vibration data points with respective ones of theone or more sensors based on a key associated with each of the vibrationdata points. In some examples of the methods and apparatuses describedherein, the key includes an indication of a location of a sensor, asensor function, or both.

Some examples of the methods and apparatuses described herein mayfurther include operations and features for filtering a list of the oneor more machines based on determining that the anomaly has not occurredat one or more respective sensors associated with the one or moremachines, where the indication of the one or more respective sensorsassociated with the anomaly is based on the filtered list.

As is understood by a person having ordinary skill in the art, the stepsand process shown with respect to FIG. 1 (and those of all otherflowcharts and sequence diagrams shown and described herein) may operateconcurrently and continuously, are generally asynchronous andindependent, and are not necessarily performed in the order shown.Reference has been made herein to one or more Figures; however, suchreferences are included for illustrative purposes and do not place anylimitations on any methods, systems and processes described herein.

These and other aspects, features, and benefits of the claimedinvention(s) are apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an industrial facility including a devicethat supports analyzing machine performance, in accordance with aspectsof the present disclosure.

FIG. 2 shows an example dataset that supports analyzing machineperformance, in accordance with aspects of the present disclosure.

FIG. 3 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 4 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 5 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 6 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 7 shows an example report for an example dataset that supportsanalyzing machine performance, in accordance with aspects of the presentdisclosure.

FIG. 8 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 9 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 10 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 11 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 12 shows a graph for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure.

FIG. 13 shows a graph for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure.

FIG. 14 shows a graph for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure.

FIG. 15 shows a diagram of a system including a device that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure.

FIG. 16 shows a flowchart illustrating a method that supports analyzingmachine performance in accordance with aspects of the presentdisclosure.

DETAILED DESCRIPTION

To promote an understanding of the principles of the present disclosure,reference is made to the embodiments illustrated in the drawings andspecific language is used to describe the same. Nevertheless, it isunderstood that no limitation of the scope of the disclosure is therebyintended; any alterations and further modifications of the described orillustrated embodiments, and any further applications of the principlesof the disclosure as illustrated therein are contemplated as wouldnormally occur to one skilled in the art to which the disclosurerelates. All limitations of scope should be determined in accordancewith and as expressed in the claims.

Whether a term is capitalized is not considered definitive or limitingof the meaning of a term. As used in this document, a capitalized termshall have the same meaning as an uncapitalized term, unless the contextof the usage specifically indicates that a more restrictive meaning forthe capitalized term is intended. However, the capitalization or lackthereof within the remainder of this document is not intended to benecessarily limiting unless the context clearly indicates that suchlimitation is intended.

In some cases, an industrial mill or factory may include numerousmachines and other equipment. During operation, this equipment mayvibrate, which may indicate that the equipment has, over time, incurredwear and tear or other physical damage (e.g., potentially causingfailures, etc.). If this goes undetected, the degradation that causesthe vibrations may cause major issues that may then lead to unplannedevents, such as a shutdown or other significant operational events. Suchunplanned events are undesirable because they may lead to time and/ormonetary losses for an owner and/or operator of the equipment.Accordingly, techniques are implemented to monitor such vibrations toattempt to control for and/or prevent excessive degradation of equipmentso as to avoid these potentially significant unplanned events. As such,many industrial machines include sensors that measure vibration andother physical phenomena.

Some techniques for monitoring these vibration measurements may include,for example, a system that detects when a sensor reports a vibrationmetric that satisfies or exceeds a given threshold. However, a givenindustrial machine may include hundreds of sensors, and a facility mayinclude hundreds (or more) of industrial machines. Furthermore, becausevibration sensors are subject to a variety of factors that maycompromise data integrity, a suitable solution for normalizing vibrationdata collected from sensors may be desirable. For example, previoussolutions may not properly control for noise, time series discrepancies,breakage events, and other factors that influence data analyses.

Moreover, some machines may at certain times (e.g., periodically or whenunder high load) substantially increase their operations which maycorrespondingly generate an increased level of vibration measurements.Some techniques for monitoring vibration measurements may flag such asudden change in the vibration measurements in this situation as ananomaly or as potentially problematic, but this sudden jump in thevibration measurements may not be indicative of an anomalous orexcessive amount of vibrations, and thus reporting this jump may resultin a false positive.

Accordingly, the inability to control such factors may render analysesinfeasible or inaccurate. In particular, the inability to adequatelyprocess vibration data to control for variations may add significantcomplexity to performing industrial scale assessments of machines in afacility (e.g., because comparisons between given data points may berendered useless by uncontrolled disparities between the given datapoints). Thus, techniques are described herein by which operational data(e.g., vibration measurements obtained from sensors on industrialmachinery or other equipment) may be used to detect potential issues forindustrial equipment before a failure or other potentially substantialnegative results occur due to the issues (e.g., before excessivedegradation of the equipment, which may in turn cause a catastrophicfailure, a temporary shutdown, or lead to the equipment needing to beprematurely replaced).

According to the techniques described herein, a system and device foranalyzing machine performance (e.g., for analyzing vibrationalperformance) may obtain operational data (e.g., vibration data), such asfrom one or more sensors located on or near one or more machines. Fromthe operational data, the system (or device) may perform a procedure toanalyze the operational data to detect whether the operational datacontains an anomaly, that is, a subset of the operational dataindicating that there is a possibility for an issue with the machinefrom which the data was obtained. In some examples, the system anddevice may preprocess the obtained operational data according to theinformation in the data, model the operational data to identify ananomaly and associate the anomaly with a corresponding sensor and/ormachine that may be experiencing an issue, and post-process theoperational data to report this information to an operator of thesystem. According to the report, the operator may confirm whether or notthe sensor or machine is experiencing an issue to be resolved and takeappropriate action as needed to prevent a shutdown, damage, or othersimilarly undesirable result. In some examples, these or other anomalydetection procedures may be performed periodically as a batch process,for example, every night or according to any other like schedule.

In some examples, the procedures described herein leverage the power ofparticular anomaly detection algorithms to identify an anomaly andassociate the anomaly with corresponding equipment that may experience apotential issue. For example, a device (or, e.g., several devicesdistributed in a processing system) may filter a dataset of operationaldata to obtain one or more groups (e.g., sets) of data includingmeasurements (e.g., vibration measurements) obtained via particularnumbers of the most recently performed measurement readings. Forexample, the device may filter the dataset to obtain a first group ofvibration data including the most recent 3,000 data points (i.e., the3,000 most recent measurements) and a second group of vibration dataincluding the most recent 1,000 data points (e.g., the filteringperformed in both an x-axis and a y-axis, such as by time and by avibration measure, respectively). In this way, the first group ofvibration data may correspond to a long-term window and the second groupof vibration data may correspond to a short-term window, which may beused alone or in combination to classify faults (e.g., potentialanomalies) as either “long-term” or “short-term.” In other examples, itis contemplated that the groups of vibration data may be filtered toinclude any number of data points either greater than or less than the3,000 and 1,000 data point groups described herein.

In some examples, after filtering the dataset to obtain the differentgroups of vibration data (e.g., each corresponding to different lengthwindows), the device may use an anomaly detection algorithm, such as aunivariate anomaly detection algorithm (e.g., an isolation forestalgorithm), to detect outliers in each of the sets of data points ineach respective group of vibration data. The device may then compare anamplitude of each of the outliers to a threshold for the respectivegroup of vibration data, such as via a high-pass filter, to determine asubset of the outliers with the more excessive overall vibrationreadings (e.g., the data points with the greatest measurements or theirmeasurements having the greatest deviations, etc.). In some examples,the device may determine that a cluster or certain quantity of outliersindicates (or at least potentially indicates) an anomaly associated withthe sensor(s) or device(s) from which the corresponding vibration datawas obtained.

In some examples, the device may generate a report including anindication of this anomaly (e.g., either directly by reporting theparticular outliers or indirectly via a composite indication) andcommunicate this report with an operator or engineer for the associatedfacility. In some examples, the report may also indicate whether anyinformation indicated in the report is associated with the long-termand/or the short-term windows. Based on the report, the operator orengineer may confirm whether the device(s) associated with any indicatedanomalies are indeed operating abnormally and, if needed, troubleshootthese device(s) or otherwise mitigate potential negative effects of theanomaly.

Accordingly, the techniques described herein provide early detection ofpotential mechanical and/or operational issues associated with operatingmachinery and other industrial equipment. Thus, an operator or engineerresponsible for maintaining the machinery may more quickly and moreaccurately identify these issues, thereby mitigating and/or preventingdamage and other negative effects related to these issues. Additionally,the techniques described herein may be agnostic to the particularunderlying asset and agnostic to the particular indicator or type ofdata that is used. That is, the techniques described herein foranalyzing machine performance may be implemented similarly with respectto any type of device in any applicable environment as well as withrespect to any other data or measurement type (e.g., temperature, or anyother like characteristic).

FIG. 1 shows an example of an industrial facility 100 including a device105 that supports analyzing machine performance in accordance withaspects of the present disclosure. The facility 100 may include aperformance monitoring system (e.g., a vibration monitoring system),where the performance monitoring system may include the device 105 andone or more machines 110, where the device 105 may be a server or othercomputing device (or, alternatively, multiple distributed devicesserving a similar function) configured to analyze performance of themachines 110 (e.g., vibrational performance of the machines 110). Thedevice 105 may obtain operational data (e.g., vibration data) from oneor more sensors 115 located on one or more respective machines 110(e.g., coupled with or integrated with the machines 110) or near one ormore respective machines 110 (e.g., while being in communication withthe respective machines 110).

In some examples, the device 105, such as a server or other computingdevice, may be in electronic communications with one or more of thesensors 115, where the sensors 115 are configured to collectmeasurements associated with one or more of the machines 110 in thefacility 100 (e.g., mill equipment or other machinery in an industrialfacility). The device 105 may communicate with the sensors 115 via awireless connection, such as communications using one or more radioaccess technologies including fourth generation (4G) systems such asLong-Term Evolution (LTE) systems, fifth generation (5G) systems whichmay be referred to as New Radio (NR) systems, or Wi-Fi systems (e.g.,wireless local area network (WLAN) systems). Additionally oralternatively, the device 105 may communicate with the sensors 115 via awired connection.

In some examples, an operator (or, e.g., an engineer or other personnelof the facility 100) may be stationed within or near the facility 100,and the operator may communicate with the device 105 via a userequipment (UE) 120 (e.g., a cellular telephone, a laptop, or any othercommunications device). As shown by the dashed line in FIG. 1 betweenthe indoor and outdoor UEs 120, in some examples, the operator with theUE 120 may be located in (or within a proximity to) the facility 100.Alternatively, the operator with the UE 120 may not be located near thefacility 100, such as for remote personnel (e.g., a remote engineer). Inthis case, the remote personnel may be trained and equipped tocommunicate instructions to personnel that are physically located at thefacility 100 and/or to transmit signals to machinery and other assets ofthe facility 100 from the remote location (e.g., from a corporateoffice).

In some examples, the device 105 may communicate information with the UE120 to inform the operator of the UE 120 as to a status or updates forthe machines 110, for example, according to measurements for themachines 110 received from the sensors 115. Based on the informationreceived from the device 105, the operator of the UE 120 may performmaintenance, adjustments, and the like on one or more of the machines110 and/or one or more of the sensors 115.

FIG. 2 shows an example dataset 200 that supports analyzing machineperformance in accordance with aspects of the present disclosure. Assimilarly described herein, in a vibration monitoring system, a device(e.g., a server) may collect measurements, such as vibration data, invarious formats and at different intervals, from one or more sensors(e.g., sensors 115). Before analyzing the data, the device maypreprocess the data to format the data in a way that it is organized andusable for interpretation, manipulation, and the like.

To preprocess vibration data, for example, the device may convert thedata into a long format, such as is shown by the example dataset 200 ofFIG. 2. The example dataset 200 includes several columns forcorresponding information, for example, a column for an indicator type,a date-time, and a column for measurements for each sensor.

Further to preprocess the vibration data, the device may aggregate thedata in the converted dataset 200 according to a time interval. In thisway, the device may group vibration data into one or more groups ofvibration data, where each group of vibration data corresponds to arespective time interval. For example, as shown by the example dataset200 of FIG. 2, vibration data may be grouped (e.g., aggregated) by hour,that is, according to respective time intervals of each one-hourinterval (although any other duration time interval may be usedsimilarly are thus also contemplated herein). By grouping the dataaccording to a fixed time interval, the data may be normalizedirrespective of the collection frequency of any particular sensorrelative to another sensor. That is, by normalizing the data accordingto particular fixed time intervals, data collected from a sensor thatrelatively less frequently collects data may be grouped with only thedata in one fixed time interval from another sensor that might collectdata with a substantially higher frequency. Thus, normalizing the databy time helps to prevent the data from disproportionately valuing thedata from sensors and machines that might simply report data morefrequently than other sensors and machines.

FIG. 3 shows a graph 300 for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 300 includes a firstgroup of vibration data 305 and a second group of vibration data 310.The graph 300 shows vibration data plotted according to measurements fora peak acceleration (in units of standard gravity (g)) over time (inunits of days). The example dataset shown in FIG. 3 includes a firstdataset 315 and a second dataset 320 following the first dataset 315 inthe time domain. The example dataset of FIG. 3 shows an example in whichtrend data (e.g., including the first group of vibration data 305 and/orthe second group of vibration data 310) increases over time, as shown bythe relatively normal vibration data in the first dataset 315 followedby an increasing vibration trend for the vibration data in the seconddataset 320.

After preprocessing the vibration data, the device may model thevibration data to identify an anomaly (or multiple anomalies), that is,according to a model for identifying anomalies in groups of vibrationdata. The device may accordingly associate the anomaly with acorresponding sensor and/or machine that may be experiencing an issuebased on the model. In some examples, the model uses a univariateapproach when determining whether to associate (or not to associate) ananomaly with each unique combination of sensors and indicator types.That is, by using a univariate equation dependent only on the particularcombination of sensor and indicator type, discrepancies and thusmechanical issues and the like are relatively more easily identifiable.

Additionally or alternatively, the model may use an artificialintelligence (AI) learning algorithm to identify anomalies, where thelearning algorithm may use an unsupervised learning approach toidentifying potential issues (e.g., as opposed to a supervised learningapproach). In a supervised learning approach, the device attempts topredict when an asset will fail by analyzing when a similar or analogousasset has failed in the past. A supervised learning approach, however,may require a relatively large amount of time for training and, in casesin which a sufficient amount of time has not yet been spent training thealgorithm, predictions may be less reliable (e.g., when there is notsufficient failure data based on which to learn and make predictions).Thus, rather than predict when an asset is going to fail, the model mayuse an unsupervised learning approach in which the algorithm may learnand detect vibration signatures that are indicative of potentialfailures. In some examples, the unsupervised learning algorithm used bythe model may similarly account for substantially any type ofmeasurement, indicator type, and/or asset. As such, the model's learningalgorithm may, in some cases, periodically retrain (e.g., on a nightlybasis, or according to any other like schedule). Thus, the model is notlimited to a particular indicator type and may be quickly retrained toprovide accurate predictions for any number of differentimplementations.

In some cases, when using predictive maintenance, vibration data thatindicates an increasing vibration trend may be a sign that there is anissue with the underlying asset (e.g., an anomaly). That is, whenvibration trend data increases, the vibration data tends to include datapoints that are outside of the normal operating behavior. Accordingly,the techniques described herein detect, and over time learn to betterdetect, these increases in trend data. By using anomaly detection, thedevice may determine when the vibration trend data extends (or is likelyto extend) beyond a normal level of vibration activity.

FIG. 3 depicts the first dataset 315 and a second dataset 320 followingthe first dataset 315 in the time domain. The example dataset shown bythe graph 300 of FIG. 3 shows that the first dataset 315 includesrelatively normal vibration patterns, that is, where the device may notdetermine (or may be relatively less likely to determine) that thevibration data 305 and/or the vibration data 310 includes an anomaly.

The second dataset 320, however, includes vibration patterns (e.g., avibration signature) with an increasing vibration trend, which may be(or be similar enough to) a vibration pattern that the device haslearned to detect (e.g., via an unsupervised learning algorithm). Thatis, based on the increasing vibration trend of the vibration data 305and the vibration data 310 in the second dataset 320, the device maydetermine (or may be relatively more likely to determine) that thevibration data 305 and/or the vibration data 310 includes an anomaly.

In some examples, the device may additionally be configured withparameters for performing debugging operations (e.g., debuggingparameters) and/or for performing performance optimizations (e.g.,performance parameters). In some cases, the device may be configuredwith the debugging parameters and/or the performance parameters beforeusing the model to identify an anomaly in the vibration data and toassociate the anomaly with a corresponding sensor and/or machine thatmay be experiencing an issue. In some cases, the device be configuredwith a condition that the debugging parameters and/or the performanceparameters be configured before using the model, or, alternatively, thedevice be configured with one or more default values for the debuggingparameters and/or the performance parameters (or, e.g., may beconfigured to bypass the debugging operations and/or the performanceoptimizations).

In some examples, the device may perform debugging operations accordingto one or more debugging parameters to troubleshoot the vibrationmonitoring system described herein. The debugging parameters may includeone or more of: a debug parameter (e.g., “debug”—a parameter that may betoggled between “True” and “False,” and when set to “True” indicatesthat verbose debugging messages are to be printed on a console fordebugging by a user); a plotting parameter (e.g., “plot_all_charts”—aparameter that may be toggled between “True” and “False,” and when setto “True” informs the algorithm to create charts of the trend overlayedwith highlights of the anomalies), and/or a data description (e.g.,“data_description”—a suffix that may be used to identify differentdebugging plots, such as to differentiate data from differentfacilities). In some examples, the debugging operations and debuggingparameters may not affect how the model performs, but rather may be usedonly for debugging operations to identify and/or troubleshoot issueswith the vibration monitoring system itself.

In some examples, the device may perform performance optimizationsaccording to one or more performance parameters to optimize one or moreparameters for using the model to identify an anomaly in the vibrationdata and to associate the anomaly with a corresponding sensor and/ormachine as described herein. The performance parameters may include oneor more of: data (e.g., data that has been received from one or moresensors, has been preprocessed, and is to be modeled); a date-timecolumn indicator (e.g., “datetime_column”—identifying a name for acolumn that includes date-time information); an indicator column type(e.g., “indicator_type_column”—identifying a name for a column thatincludes indicator type information); a window size parameter (e.g.,“window_size”—identifying sizes for short-term and/or long-term windowsizes, to be used by the algorithm described herein; and may have one ormore default values configured, such as 1,000 and 3,000, respectively);a number of processors (e.g., “cpus”—an indication of a number ofprocessors (cores) to use for the model as described herein, which maybe optimized for multi-core processor technologies such as parallelprocessing, and where a greater value for the parameter may provide ahigher processing speed and shorter time spent to perform the modellingprocedures); and/or a sensitivity parameter (e.g.,“sensitivity”—controlling how sensitive the model is to detectinganomalies in the trend data; and may have a default value configured,such as 0.01, and may be programmatically updated to increase ordecrease a sensitivity to respectively identify more or less datasets aspotentially including an anomaly).

FIG. 4 shows a graph 400 for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 400 includes onegroup of vibration data 410 (as identified according to the concatenatedindicator type information in the key:“CrossettPaperMillC3PaperMachine4thDryerSection5125404547BottGearC4C78AXA”).The graph 400 shows the vibration data 410 plotted according tomeasurements for an overall vibration (in units of inches per second(in/sec)) over a number of recent data points. The example dataset ofFIG. 4 shows an example in which trend data (e.g., a trend of thevibration data 410) increases over time, as shown by a trendline 412having a positive slope.

As shown by the graph 400 in FIG. 4, the vibration data 410 hasgenerally trended higher over the time taken to obtain each of the datapoints shown (e.g., a small number of days). According to the techniquesdescribed herein, the model is shown to have detected that an anomaly islikely in the vibration data 410 based on the levels of the overallvibration measurements satisfying a corresponding vibration threshold430 (i.e., that there may potentially be a problem occurring at arespectively associated machine when the overall vibration level reachedrelatively high levels that satisfy or exceed the correspondingvibration threshold 430).

The model, as described herein, may include a series of steps (e.g.,each of, or a portion thereof, in combination) that may be performed atthe device to identify anomalous trend data. While the data is describedherein as vibration data 410 for identifying anomalies in vibrationdata, the model and other procedures described herein may be implementedsimilarly for other types of operational data.

In some examples, the model may include a step in which the deviceverifies the structure of the vibration data 410, for example, byconfirming that the vibration data 410 has a column for an indictor typeand for a datetime. If the device determines that the vibration data 410does not include this column for an indictor type and for a datetime,the device may return to the preprocessing procedures as describedherein.

In some examples, the model may include a step in which the devicefilters the vibration data 410 (e.g., filtering the vibration data 410as may have been grouped together for each time interval, as describedherein). The device may filter the vibration data 410 to obtain one ormore groups of vibration data 410, for example, a first group ofvibration data 410 that includes the most recent 1,000 data points and asecond group of vibration data 410 that includes the most recent 3,000data points. The particular numbers stated here (i.e., 1,000 and 3,000)for filtering the data point count of the vibration data 410 mayapproximately correspond to data gathered over windows of one month(e.g., a short-term window) and three months (e.g., a long-term window),respectively, for example, when using one-hour time intervals forgrouping the vibration data 410. Other numbers of data points arecontemplated herein and may be implemented similarly to correspond tolonger or shorter time periods and/or to account for longer or shortercollection frequencies or grouping time intervals. As similarlydescribed herein, one or more window size parameter may be configured toadjust the short-term and long-term windows.

In some examples, the model may include a step in which the device usesan anomaly detection algorithm, such as a univariate anomaly detectionalgorithm (e.g., an isolation forest algorithm), to detect outliers 440in each of the sets of data points in each respective group of vibrationdata 410 (e.g., detecting outliers 440 in the subset of data points inthe vibration data 410 for the short-term window as well as outliers 440in the subset of data points in the vibration data 410 for the long-termwindow). The isolation forest algorithm is an anomaly detectionalgorithm that detects anomalies based on path lengths associated withdata points in an “isolation tree” structure, rather than, for example,comparing data point to a model of “normal” points (the isolation forestalgorithm may be found, e.g., in the scikit-learn library in the Pythonprogramming language). For example, with reference to the exampledataset shown in the graph 400 of FIG. 4, a set of parameters for theisolation forest algorithm may include: an “n_estimators” parameter setto 150; a “max_samples” parameter set to 500; a “contamination”parameter set to 0.01 (as may be defined as equal to a value configuredfor a sensitivity parameter); a “max features” parameter set to 1; a“random state” parameter set to 864; and a “bootstrap” parameter toggled(e.g., between “True” and “False) to False. It is noted that thesespecific parameters and correspondingly configured values are only oneexample, and any other operable configuration may be implementedsimilarly. Likewise, although the use of the isolation forest algorithmis described herein, any other like algorithm for detecting anomaliesmay be implemented similarly.

In some examples, the model may include a step in which the devicefilters the outliers 440 obtained via the anomaly detection algorithm,for example, via a high-pass filter. Because the anomaly detectionalgorithm (e.g., such as the isolation forest algorithm) may returnpoints that are outliers 440 on both of the higher and the lower sidesof the normal operation band, the high-pass filter may be applied to theoutput from the anomaly detection algorithm to obtain a subset of theoutliers 440 that have values on the higher side of the normal operationband and/or that have the greatest values among all of the outliers 440returned by the anomaly detection algorithm.

FIG. 5 shows a graph 500 for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 500 includes onegroup of vibration data 510 (as identified according to the concatenatedindicator type information in the key:“CrossettPaperMillC3WinderWinderFrameDSA41BXH”). The graph 500 shows thevibration data 510 plotted according to measurements for an overallvibration (in units of in/sec) over a number of recent data points. Theexample dataset of FIG. 5 shows an example in which trend data (e.g., atrend of the vibration data 510) neither substantially decreases norsubstantially increases over time. According to the techniques describedherein, the model is shown to have detected that an anomaly is likely inthe vibration data 510 based on the levels of the overall vibrationmeasurements satisfying a corresponding vibration threshold 530 (i.e.,that there may potentially be a problem occurring at a respectivelyassociated machine when the overall vibration level reached relativelyhigh levels that satisfy or exceed the corresponding vibration threshold530).

As shown by the graph 500 in FIG. 5, not all outliers or anomalousactivity are necessarily preceded by a trend. Rather, outliers andanomalous activity may, in many cases, occur as extreme spikes inactivity (e.g., as may be caused by unpredictable or non-standard forceson a corresponding machine, such as by an external force orinterruption). The model, as described herein, may also detect theseoutliers and anomalies that are not preceded by a discernable trend.

For example, as shown in the graph 500 of FIG. 5, outliers 540 are shownfor the most recent 1,000 data points (e.g., corresponding toapproximately one month of measurements) for the overall vibrationmeasurements. The outliers 540 shown in FIG. 5 are the result ofapplying an isolation forest algorithm to detect a set of outliers andthen determining the highlighted outliers 540 to be potential anomaliesby comparing each outlier of the set of outliers obtained from theisolation forest algorithm to a threshold 520, where the threshold 520is set at a 97.5% percentile for the group including the most recent1,000 data points. Thus, the remaining highlighted outliers 540 shown inFIG. 5 are determined according to the algorithm as the outliers 540 ina short-term window (of the most recent 1,000 data points) that have thegreatest amplitudes of the entire set of outliers obtained via theisolation forest algorithm.

In some examples, the device may determine whether the outliers 540 ofthe vibration data 510 include an anomaly (e.g., associated with one ormore sensors and one or more assets) based on a quantity of outliers 540of the short-term window that are within a detection window (e.g., 24hours), a quantity of outliers of a long-term window that are alsowithin the detection window, and/or a relation between the quantities ofoutliers 540 within the detection window for the short-term and thelong-term windows. That is, the device may count a number of outliers540 that occurred for both the short-term window and the long-termwindow in the last 24 hours (i.e., in a 24-hour detection window).

The number of outliers 540 in the last 24 hours that occurred for boththe short-term window and the long-term window may be used to identify“normal” behavior of an asset over different operating periods. In someexamples, behavior that is considered normal in one period may beconsidered abnormal in another operating period (e.g., the same recentdata points may be considered normal behavior in the short-term windowwhile also being considered abnormal in the long-term window, or viceversa). Thus, the number of outliers identified over the last 24 hours(i.e., the detection period) may be different for each different windowlength.

FIG. 6 shows a graph 600 for an example dataset that supports analyzingmachine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 600 includes onegroup of vibration data 610 (as identified according to the concatenatedindicator type information in the key:“CrossettPaperMillC3MezzFan510382RoofAirSupplyFan3MOB6063XA”). The graph600 shows the vibration data 610 plotted according to measurements foran overall vibration (in units of in/sec) over a number of recent datapoints. The example dataset shown in FIG. 6 includes a first dataset 615and a second dataset 620 following the first dataset 615 in the timedomain. The example dataset of FIG. 6 shows an example in which the useof multiple windows for the model described herein are needed to predictoutliers 640 after, for example, an anticipated and persistent orpermanent (or semi-permanent) change in the vibration data 610.

The example dataset of FIG. 6 shows an example in which the vibrationdata 610 of the first dataset 615 represents an “old normal” and thevibration data 610 of the second dataset 620 represents a “newnormal”—that is, an intentional or predicted shift in the level of thevibration data 610 from the first dataset 615 to the second dataset 620.

The short-term window (including, e.g., the 1,000 most recent datapoints), the long-term window (including, e.g., the 3,000 most recentdata points), and the detection window (e.g., a 24-hour window) arerolling windows that may account for changes from between different“normals,” for example, as shown by the graph 500 of the vibration data610 in FIG. 6. Thus, the model, as described herein, may use thedifferent rolling windows to determine that the change in the vibrationdata 610 from the first dataset 615 to the second dataset 620 is notnecessarily an anomaly.

According to the model, the device may use the vibration data 610 of themost recent data points to establish normalcy and identify the outliers640 according to the “new normal” (e.g., the “new normal” as per thesecond dataset 620). If only the combination of the vibration data 610in the first dataset 615 with the second dataset 620 were considered,the device would not correctly identify the outliers 640 shown in FIG. 6(e.g., the device would determine a substantially higher threshold 630due to counting the first dataset 615). Thus, whereas another system ordevice that only considers the combined, complete window across both ofthe first dataset 615 and the second dataset 620 may switch to anoffline state for a duration of time to retrain a new set ofhyperparameters (e.g., doing so each night to retrain), the device foranalyzing machine performance as described herein can accurately set athreshold 630 based on the second dataset 620 and not on the firstdataset 615. Accordingly, the device may accurately and quickly identifythe outliers 640 (and thus correspondingly, the anomalies) in the trenddata of the second dataset 620 without, for example, switching offlineto retrain a new set of hyperparameters.

Accordingly, in some examples, the device records a number of outliersthat occurred in the last 24 hours both for the short-term window andfor the long-term window. In some examples, the device may store thisinformation, with other information such as metadata including one ormore of a facility name, a sensor name, a reporting date, a facilityidentifier (e.g., a facility id), and/or an indicator (as well as any orall of the other information described herein) in a storage medium(e.g., memory 1530). In some examples, this storage location may be orinclude non-transitory computer-readable media at the device or that thedevice may access, such as random-access memory (RAM), read-only memory(ROM), electrically erasable programmable ROM (EEPROM), flash memory, acompact disk (CD) ROM or other optical disk storage, a magnetic diskstorage or other magnetic storage devices, or any other non-transitorymedium that can be used to carry or store desired program code means inthe form of instructions or data structures and that can be accessed bya general-purpose or special-purpose computer, or a general-purpose orspecial-purpose processor. Additionally or alternatively, the device maystore the information at a remote device or via a remote service, suchas via a cloud storage system, a cloud storage service, and/or at a datawarehouse that the device may access using wireless communications, forexample.

FIG. 7 shows an example report 700 for an example dataset that supportsanalyzing machine performance, in accordance with aspects of the presentdisclosure. As similarly described herein, in a vibration monitoringsystem, a device may post-process the vibration data that has beenmodeled to identify an anomaly and associate the anomaly with acorresponding sensor and/or machine that may be experiencing an issue.The device may post-process the modeled vibration data to produce andshare a report of the modeled data.

In some examples, post-processing may be the last step in detectinganomalous trend data. During post-processing, the device may generate areport, such as is shown by the example report 700 of FIG. 7. The report700 may include several columns for corresponding information. Forexample, as similarly shown by the example report 700 of FIG. 7, thereport 700 may include columns for one or more of: a facility name(e.g., “facility”); a sensor name (e.g., “tag_name”); a number ofshort-term outliers (e.g., “short_term_num_outliers”); a number oflong-term outliers (e.g., “long_term_num_outliers”); a report date(e.g., “report_date”); a facility identifier (e.g., “facility_id”);and/or an indicator type (e.g., “indicator”).

In some examples, to account for noise and randomness, a sensor may beconfigured not to report information if a number of outliers does notsatisfy a reporting threshold and to report the information if thereporting threshold for the number of outliers is met. For example, thereporting threshold may be satisfied with at least four short-termoutliers and/or at least four long-term outliers to report. Accordingly,in this example, a sensor that has three or fewer short-term and/orthree or fewer long-term outliers to report may not report theseoutliers. In some cases, the reporting threshold may be configured atsuch a level that failing to satisfy the reporting threshold isindicative of only relatively minor issues (or no issues at all). Thus,in some examples, the sensor having fewer outliers to report than thereporting threshold may be regarded as relatively lower priority,because it may be substantially unlikely for a significant problem tooccur in this situation.

In contrast, sensors having a relatively greater number of short-termoutliers and/or long-term outliers tend to have more severe issues, andtherefore these sensors may be assigned a higher priority. For example,in the illustrative example shown by the report 700 of FIG. 7, thereport shows that the sensor named “sensor8” has indicated a higheroutlier count for the short-term outliers (having 17 compared to each ofthe other sensors having between zero and nine) as well as a higheroutlier count for the long-term outliers (having 11 compared to each ofthe other sensors having between three to five). Thus, as a result, the“sensor8” sensor may, in some examples, be the first (or one of thefirst) sensors to be investigated based on the information indicated inthe report 700.

As similarly described herein, the report 700 is generated for thevibration monitoring system to transmit information to one or moreadditional personnel, such as to a remote engineer, an administrator,and/or other like personnel to report the results of the model. Thistransmitted information may be used to rectify problematic situations,perform maintenance, turn off critical machinery, and/or other likeoperations that may be performed to prevent, avoid, or mitigate thepotential negative effects of an anomaly or other problem. For example,the device may transmit the report 700 to a remote engineer (e.g., via awireless communications network), the remote engineer may receive thereport 700 from the device, and the remote engineer may analyze theinformation indicated in the report 700 to determine whether actionsshould be taken based on the report. In some examples, the remoteengineer may analyze the report 700 and create a short list of assets towhich to give attention, where the short list may exclude one or moreassets for which the information in the report 700 indicates are inrelatively little need of maintenance (or are relatively unlikely tosoon need maintenance).

In some examples, the remote engineer (or other personnel) may analyzethe information indicated via the report 700 and determine a ranking ora priority list for each of the different assets included in the report700 (e.g., ranking different machinery according to a number ofshort-term outliers, a number of long-term outliers, or both, to createa priority for addressing each of the machinery). In this example, ifthe remote engineer determines from the report 700 that an action shouldbe taken to prevent, avoid, and/or mitigate a negative unplanned eventat a high priority asset (as similarly described herein), the remoteengineer may, for example, transmit an instruction to one or moredevices at the correspondence facility and/or to additional personnelpresent at or near the facility. In such a scenario, an instruction maybe to power off certain machinery, modify a performance of certainmachinery, activate certain machinery, and/or any other operations thatmay be performed at an industrial facility to affect the performance ofthe machinery at the facility.

In some examples, the number of short-term outliers and the number oflong-term outliers, as indicated in the report 700, may substantiallyagree, that is, the report 700 may indicate roughly similar numbers forthese two metrics. In such cases, a relatively low indicated number forboth of the short-term and the long-term outliers may indicate that thecorresponding asset is at a relatively low risk of a negative issueoccurring that would require assistance (e.g., from a remote engineer).Likewise, a relatively high indicated number for both of the short-termand the long-term outliers may indicate that the corresponding asset isat a relatively high risk of a negative issue occurring, and thus thisasset should likely be prioritized for review (e.g., by a remoteengineer). Alternatively, in some examples, the number of short-termoutliers and the number of long-term outliers as indicated in the report700 may meaningfully differ, that is, the report 700 may indicatesubstantially different numbers for these two metrics.

In such cases, this information may be directed to personnel such as aremote engineer to determine whether or not an anomaly is likely to haveoccurred. For example, the report 700 may indicate that the number ofshort-term outliers is relatively high while the number of long-termoutliers is relatively low. In this example, a remote engineer mayreview the information in the report 700 and determine whether thisdiscrepancy indicates that an anomaly is likely (e.g., in the case of arapid increase in a number of short-term outliers due to a malfunction,outside interference, etc.) or that an anomaly is not likely (e.g., inthe case of a known or pre-planned change to a “new normal” for theasset). Accordingly, engineers (and other trained personnel) can utilizethe techniques described herein to relatively more accurately andquickly identify occurrences of anomalies at industrial assets so thatactions may be taken to prevent, mitigate, and/or avoid a negative eventand/or its associated costs.

As described below, each of FIGS. 8 through 14 provides an example inwhich a machine learning anomaly detection model (e.g., operatingaccording to the techniques described herein) may determine anoccurrence of an anomaly at an industrial asset (in some cases, referredto as a “catch”). Accordingly, in these examples, a warning indicationmay be provided to an operator of the industrial asset based ondetermining the occurrence of the respective anomalies. FIGS. 8 through11 provide examples of “long-term catches” that each indicate an anomalyassociated with a long-term window. FIGS. 12 through 14 provide examplesof “short-term catches” that each indicate an anomaly associated with ashort-term window.

FIG. 8 shows a graph 800 for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 800 includes a firstgroup of vibration data 805 (indicated to be based on a y-axismeasurement of velocity) and a second group of vibration data 810(indicated to be based on an x-axis measurement of acceleration). Thegraph 800 shows vibration data plotted according to measurements for apeak acceleration (in units of standard gravity (g)) over time (in unitsgiven by dates ranging from March 1 to August 30). FIG. 8 provides anexample of a “long-term catch” indicating that an anomaly associatedwith a long-term window was identified.

At 815, the graph 800 shows that a first anomaly was detected on May 5.In this example, the first anomaly at 815 was detected using thetechniques described herein for analyzing vibrational performance. Forexample, the first anomaly may have been detected based on one or moreoutliers detected in the dataset in the first group of vibration data805 and/or the second group of vibration data 810, for example, based ona number of these detected outliers in a long-term window preceding thedetection of the first anomaly on May 5 (e.g., within 3,000 data points,which may correspond to approximately three months).

At 820, the graph 800 shows that a second anomaly was detected on June23. In this example, the second anomaly at 820 was detected using thetechniques described herein for analyzing vibrational performance. Forexample, the second anomaly may have been detected based on one or moreoutliers detected in the dataset in the first group of vibration data805 and/or the second group of vibration data 810, for example, based ona number of these detected outliers in a long-term window preceding thedetection of the second detected anomaly on June 23 (e.g., within 3,000data points, which may correspond to approximately three months).

The graph 800 also shows at 891, 892, and 893, three occurrences ofevents indicated by “SF.” The three occurrences of events may indicatethat, for a short timeframe, there was a peak acceleration or that thethree highest peak-acceleration values occurred at the events indicatedby “SF.”

The graph 800 of FIG. 8 also shows that, at 870, a work order wascompleted on August 22 relating to the first anomaly reported on May 5.In some examples, a “days to failure” parameter, or other likeinformation, may not be provided and/or known at the time that ananomaly is determined and transmitted to personnel (e.g., a remoteengineer), for example as shown in FIG. 8, on May 5 for the firstanomaly and on June 23 for the second anomaly. The work order completedat 870 on May 5 corresponds to the first anomaly reported on May 5.Thus, this work order was completed 109 days after the anomaly was firstreported. In some examples, the priority of a particular work order maybe based on a relative priority of the anomaly with which the work orderis associated.

While each anomaly that is determined and reported will typicallyreceive a corresponding work order, the completion of the work order at870 is the only example in FIGS. 8 through 14 showing the completion ofthe work stemming from such a work order. Although not shown withrespect to the second anomaly reported at 820 on June 23, or withrespect to the remaining reported anomalies shown and described withreference to FIGS. 9 through 14, each reported anomaly is likely to havea corresponding work order. Each work order for each of the reportedanomalies may be resolved in an amount of time that may be similar tothe 109 days shown here with reference to FIG. 8, or may be completed ina smaller or greater number of days (e.g., based on demand, cost,complexity of the work order, among other like factors).

Additionally, the graph 800 of FIG. 8 does not show that these or anyother anomalies were detected using techniques other than the techniquesfor analyzing vibrational performance as described herein. This may,although need not, indicate that the techniques described herein wereable to determine the occurrence of the first anomaly at 815 and thesecond anomaly at 820 when other previously existing techniques may nothave been able to do so.

FIG. 9 shows a graph 900 for an example dataset that supports analyzingmachine performance, in accordance with aspects of the presentdisclosure. The example dataset shown in graph 900 includes a firstgroup of vibration data 905 (indicated to be based on an x-axisvibration measurement) and a second group of vibration data 910(indicated to be based on a y-axis vibration measurement). The graph 900shows vibration data plotted according to measurements for an overallvibration (in units of in/sec) over time (in units given by datesranging from March 8 to August 30). FIG. 9 provides an example of a“long-term catch” indicating that an anomaly associated with a long-termwindow was identified.

At 915, the graph 900 shows that an anomaly was detected on April 9. Inthis example, the anomaly at 915 was detected using the techniquesdescribed herein for analyzing vibrational performance. For example, theanomaly may have been detected based on one or more outliers detected inthe dataset in the first group of vibration data 905 and/or the secondgroup of vibration data 910, for example, based on a number of thesedetected outliers in a long-term window preceding the detection of theanomaly on April 9 (e.g., within 3,000 data points, which may correspondto approximately three months).

Additionally, the graph 900 of FIG. 9 does not show that these or anyother anomalies were detected using techniques other than the techniquesfor analyzing vibrational performance as described herein. This may,although need not, indicate that the techniques described herein wereable to determine the occurrence of the anomaly at 915 when otherpreviously existing techniques may not have been able to do so.

FIG. 10 shows a graph 1000 for an example dataset that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 1000 includes a firstgroup of vibration data 1005 (indicated to be based on an x-axisvibration measurement) and a second group of vibration data 1010(indicated to be based on a y-axis vibration measurement). The graph1000 shows vibration data plotted according to measurements for anoverall vibration (in units of in/sec) over time (in units given bydates ranging from May 31 to August 23). FIG. 10 provides an example ofa “long-term catch” indicating that an anomaly associated with along-term window was identified.

At 1015, the graph 1000 shows that a first anomaly was detected on June21. In this example, the first anomaly at 1015 was detected using thetechniques described herein for analyzing vibrational performance. Forexample, the first anomaly may have been detected based on one or moreoutliers detected in the dataset in the first group of vibration data1005 and/or the second group of vibration data 1010, for example, basedon a number of these detected outliers in a long-term window precedingthe detection of the first anomaly on June 21 (e.g., within 3,000 datapoints, which may correspond to approximately three months).

At 1020, the graph 1000 shows that a second anomaly was detected onAugust 5. In this example, the second anomaly at 1020 was detected usingthe techniques described herein for analyzing vibrational performance.For example, the second anomaly may have been detected based on one ormore outliers detected in the dataset in the first group of vibrationdata 1005 and/or the second group of vibration data 1010, for example,based on a number of these detected outliers in a long-term windowpreceding the detection of the second anomaly on August 5 (e.g., within3,000 data points, which may correspond to approximately three months).

Additionally, the graph 1000 of FIG. 10 does not show that these or anyother anomalies were detected using techniques other than the techniquesfor analyzing vibrational performance as described herein. This may,although need not, indicate that the techniques described herein wereable to determine the occurrence of the first anomaly at 1015 when otherpreviously existing techniques may not have been able to do so.Moreover, an amplitude of the data points represented by the first groupof vibration data 1005 and the second group of vibration data 1010 maybe too low for at least some other techniques for analyzing vibrationalperformance, such as for root cause analysis (RCA) (e.g., the amplitudesof the data points within the first group of vibration data 1005 and thesecond group of vibration data 1010 are fully between 0.025 and 0.090in/sec).

FIG. 11 shows a graph 1100 for an example dataset that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 1100 includes a firstgroup of vibration data 1105 (indicated to be based on an x-axismeasurement of acceleration) and a second group of vibration data 1110(indicated to be based on a y-axis measurement of a height). The graph1100 shows vibration data plotted according to measurements for anoverall vibration (in units of in/sec) over time (in units given bydates ranging from March 1 to August 30). FIG. 11 provides an example ofa “long-term catch” indicating that an anomaly associated with along-term window was identified.

At 1115, the graph 1100 shows that an anomaly was detected on July 4. Inthis example, the anomaly at 1115 was detected using the techniquesdescribed herein for analyzing vibrational performance. For example, theanomaly may have been detected based on one or more outliers detected inthe dataset in the first group of vibration data 1105 and/or the secondgroup of vibration data 1110, for example, based on a number of thesedetected outliers in a long-term window preceding the detection of theanomaly on July 4 (e.g., within 3,000 data points, which may correspondto approximately three months). FIG. 11 also shows a first threshold1150.

Additionally, the graph 1100 of FIG. 11 does not show that these or anyother anomalies were detected using techniques other than the techniquesfor analyzing vibrational performance as described herein. This may,although need not, indicate that the techniques described herein wereable to determine the occurrence of the anomaly at 1115 when otherpreviously existing techniques may not have been able to do so.Moreover, an amplitude of the data points represented by the first groupof vibration data 1105 and the second group of vibration data 1110 maybe too low for at least some other techniques for analyzing vibrationalperformance, such as for RCA (e.g., the amplitudes of the data pointswithin the first group of vibration data 1105 and the second group ofvibration data 1110 are fully between 0.01 and 0.14 in/sec).

FIG. 12 shows a graph 1200 for an example dataset that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 1200 includes a firstgroup of vibration data 1205 (indicated to be based on an x-axismeasurement of acceleration) and a second group of vibration data 1210(indicated to be based on a y-axis measurement of movement in the radialdirection “R”). The graph 1200 shows vibration data plotted according tomeasurements for an overall vibration (in units of in/sec) over time (inunits given by dates labeled from July 27 to August 25). FIG. 12provides an example of a “short-term catch” indicating that an anomalyassociated with a short-term window was identified.

At 1215, the graph 1200 shows that an anomaly was detected on August 11.In this example, the anomaly at 1215 was detected using the techniquesdescribed herein for analyzing vibrational performance. For example, theanomaly may have been detected based on one or more outliers detected inthe dataset in the first group of vibration data 1205 and/or the secondgroup of vibration data 1210, for example, based on a number of thesedetected outliers in a long-term window preceding the detection of theanomaly on August 11 (e.g., within 1,000 data points, which maycorrespond to approximately one month).

Additionally, at 1215 on August 11, the anomaly was detected via RCA,based on which an RCA alert was placed on the asset (e.g., a pump). At1220, the graph 1200 shows that a “Hi Dynamic Alert” was also issuedbased on an anomaly detected on August 15. At 1225, the graph 1200 showsthat a “HiHi Dynamic Alert” was also issued based on an anomaly detectedon August 17. At 1230, on August 18, a further anomaly was detected viaRCA, based on which a second RCA alert was placed on the asset. FIG. 12also shows a first threshold 1250, a second threshold 1255, and a thirdthreshold 1260. Any of the detected anomalies 1215, 220, 1225, and 1230may have been detected based on satisfying any of the first threshold1250, a second threshold 1255, and/or a third threshold 1260

FIG. 13 shows a graph 1300 for an example dataset that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 1300 includes a firstgroup of vibration data 1305 (indicated to be based on a horizontalmeasurement of velocity) and a second group of vibration data 1310(indicated to be based on a vertical measurement of velocity). The graph1300 shows vibration data plotted according to measurements for a peakvelocity (in units of in/sec) over time (in units given by dates labeledfrom July 27 to August 25). FIG. 13 provides an example of a “short-termcatch” indicating that an anomaly associated with a short-term windowwas identified.

At 1315, the graph 1300 shows that an anomaly was detected on August 12.In this example, the anomaly at 1315 was detected using the techniquesdescribed herein for analyzing vibrational performance. For example, theanomaly may have been detected based on one or more outliers detected inthe dataset in the first group of vibration data 1305 and/or the secondgroup of vibration data 1310, for example, based on a number of thesedetected outliers in a long-term window preceding the detection of theanomaly on August 12 (e.g., within 1,000 data points, which maycorrespond to approximately one month).

Additionally, at 1320 on August 13, an anomaly (likely the same as theoriginal anomaly detected at 1315) was detected via RCA. As shown here,RCA detected the anomaly the day after the techniques that were used toinitially detect the anomaly at 1315 on August 12. Thus, assuming theseanomalies to be related to the same underlying cause(c), the techniquesprovided herein detected the anomaly one day earlier than RCA.

At 1325, the graph 1300 shows that a “Hi Dynamic” alert was issued onAugust 17 based on an anomaly detected on August 12 and/or on August 13.FIG. 13 also shows a first threshold 1350, a second threshold 1355, anda third threshold 1360. Any of the detected anomalies 1315, 1320, and1325 may have been detected based on satisfying any of the firstthreshold 1350, a second threshold 1355, and a third threshold 1360.

FIG. 14 shows a graph 1400 for an example dataset that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The example dataset shown in the graph 1400 includes a firstgroup of vibration data 1405 (indicated to be based on an x-axismeasurement of peak acceleration) and a second group of vibration data1410 (indicated to be based on a y-axis measurement of peakacceleration). The graph 1400 shows vibration data plotted according tomeasurements for a peak acceleration (in units of standard gravity (g))over time (in units given by dates ranging from March 1 to August 30).FIG. 14 provides an example of a “short-term catch” indicating that ananomaly associated with a short-term window was identified.

At 1415, the graph 1400 shows that an anomaly was detected on July 8. Inthis example, the anomaly at 1415 was detected using the techniquesdescribed herein for analyzing vibrational performance. For example, theanomaly may have been detected based on one or more outliers detected inthe dataset in the first group of vibration data 1405 and/or the secondgroup of vibration data 1410, for example, based on a number of thesedetected outliers in a long-term window preceding the detection of theanomaly on July 8 (e.g., within 1,000 data points, which may correspondto approximately one month).

At 1425, the graph 1400 shows that a “Hi Dynamic” alert was issued onJuly 18 based on the anomaly detected on July 8 at 1415. Additionally,at 1425 on August 7, an anomaly (although potentially the same as theoriginal anomaly detected at 1415) was detected via RCA. As shown here,RCA detected the anomaly 30 days after the techniques that were used toinitially detect the anomaly at 1415 on July 8. Thus, if these anomaliesare related to the same underlying cause(s), the techniques providedherein identified detected the anomaly 30 days earlier than RCA.

FIG. 15 shows a diagram of a system 1500 including a device 1505 thatsupports analyzing machine performance, in accordance with aspects ofthe present disclosure. The device 1505 may be an example of or includethe components of the device 105 as described with reference to FIG. 1and as also described throughout FIGS. 2 through 14, or a server, UE, orother computing device. The device 1505 may include components forbi-directional voice and data communications including components fortransmitting and receiving communications, including a vibrationalperformance manager 1510, an input/output (I/O) controller 1515, atransceiver 1520, an antenna 1525, memory 1530, and a processor 1540.These components may electronically communicate with one another via oneor more buses 1545.

The vibrational performance manager 1510 may include multiple componentsthat, in combination, facilitate the vibrational performance manager1510 to receive and preprocess operational data, model the operationaldata to identify an anomaly and associate the anomaly with acorresponding sensor and/or machine 110 that may be experiencing anissue, and post-process the operational data to report this informationto an operator of the system. More particularly, the vibrationalperformance manager 1510 may include a vibration data module 1550, anoutlier module 1555, and an anomaly module 1560 which may provide eachof these functions (and, e.g., may in some cases, be integrated circuitcomponents of the vibrational performance manager 1510).

In some examples, the vibration data module 1550 may receive vibrationdata points from one or more sensors associated with one or moremachines 110 (e.g., via one or more signals received from one or moremachines 110 via the antenna 1525 and the transceiver 1520). In someexamples, the vibration data points may include one or more of: a peakvelocity value, a peak acceleration value, an overall root mean squarevalue, an overall vibration value, or a combination thereof. In someexamples, the vibration data module 1550 may collect the vibration dataat discrete intervals.

The vibration data module 1550 may group the vibration data points intoa first group of vibration data and a second group of vibration data,where the first group of vibration data may include a first set of thevibration data points associated with a short-term window and the secondgroup of vibration data may include a second set of the vibration datapoints associated with a long-term window, each of the first group ofvibration data and the second group of vibration data corresponding toone of a plurality of time intervals. In some examples, each timeinterval of the plurality of time intervals may correspond to arespective hour interval (e.g., according to an hourly periodicity). Insome examples, the vibration data module 1550 may associate thevibration data points with respective ones of the one or more sensorsbased on a key associated with each of the vibration data points. Insome examples, the key may include an indication of a location of asensor, a sensor function, or both.

In some examples, grouping the vibration data points may includenormalizing the vibration data of the first group of vibration data andthe second group of vibration data.

In some examples, the vibration data module 1550 may modify the firstset of vibration data points, the second set of vibration data points,or both, based on one or more performance parameters, where grouping thevibration data points may include filtering the vibration data pointsbased on the modified data points. In some examples, the one or moreperformance parameters may include one or more of: a processed sensormeasurement, a date-time indicator, an indicator type, a window sizeparameter, a number of processors, a sensitivity parameter, or acombination thereof. In some examples, the vibration data module 1550may associate a plurality of indicators with each of the vibration datapoints, each indicator of the plurality of indicators being common to atleast a subset of the one or more sensors. In some examples, theplurality of indicators may include one or more of: a sensor type, acorresponding machine 110, a corresponding facility, an indicator type,a date-time, a sensor measurement, or a combination thereof.

In some examples, the vibration data module 1550 may pass a set ofinformation bits to the outlier module 1555, and the outlier module 1555may receive the set of information bits from the vibration data module1550, where the set of information bits may indicate the first andsecond groups of vibration data. In some examples, the outlier module1555 may detect one or more outliers from the first set of vibrationdata points and from the second set of vibration data points (e.g., asthe outlier module 1555 may have received from the vibration data module1550).

In some examples, detecting the one or more outliers may be based on oneor more of the plurality of indicators, respectively, for the firstgroup of vibration data, the second group of vibration data, or both. Insome examples, the outlier module 1555 detecting the one or moreoutliers may include the outlier module 1555 applying an isolationalgorithm (e.g., an isolation forest algorithm) to the first set ofvibration data points of the first group of vibration data and to thesecond set of vibration data points of the second group of vibrationdata. In some examples, the outlier module 1555 may determine a firstsubset of the one or more outliers based on an amplitude of each outlierof the first subset satisfying a first threshold. Likewise, the outliermodule 1555 may determine a second subset of the one or more outliersbased on an amplitude of each outlier of the second subset satisfying asecond threshold. In some examples, the first threshold is set accordingto a threshold percentile for the first set of vibration data points ofthe short-term window and the second threshold is set according to thethreshold percentile for the second set of vibration data points of thelong-term window.

In some examples, the outlier module 1555 may pass a set of informationbits to the anomaly module 1560, and the outlier module 1555 may receivethe set of information bits from the anomaly module 1560, where the setof information bits may indicate the first and second subsets of the oneor more outliers.

In some examples, the anomaly module 1560 may determine that thevibration data points include an anomaly associated with one or morerespective sensors of the one or more sensors based on a quantity ofoutliers of the first subset that are within a detection window relativeto a quantity of outliers of the second subset that are within thedetection window. In some examples, determining that the vibration datapoints include the anomaly may be based on a variation between thequantity of outliers of the first subset that are within the detectionwindow and the quantity of outliers of the second subset that are withinthe detection window. In some examples, the anomaly module 1560 mayfilter a list of the one or more machines 110 based on determining thatthe anomaly has not occurred at one or more respective sensorsassociated with the one or more machines 110, where the indication ofthe one or more respective sensors associated with the anomaly is basedon the filtered list.

In some examples, the anomaly module 1560 may transmit an indication ofthe one or more respective sensors associated with the anomaly (e.g.,via one or more signals transmitted to a UE 120 of a remote engineer viathe antenna 1525 and the transceiver 1520). In some examples, theindication of the one or more respective sensors associated with theanomaly may be based on the filtered list.

The I/O controller 1515 may manage input and output signals for thedevice 1505. The I/O controller 1515 may also manage control ofperipherals not integrated into the device 1505. In some cases, the I/Ocontroller 1515 may represent a physical connection or port to anexternal peripheral. In some cases, the I/O controller 1515 may utilizean operating system such as iOS®, ANDROID®, MS-DOS®, MS-WINDOWS®, OS/2®,UNIX®, LINUX®, or another known operating system. In other cases, theI/O controller 1515 may represent or interact with a modem, a keyboard,a mouse, a touchscreen, or a similar device. In some cases, the I/Ocontroller 1515 may be implemented as part of the processor 1540. Insome cases, a user may interact with the device 1505 via the I/Ocontroller 1515 or via hardware components controlled by the I/Ocontroller 1515.

The transceiver 1520 may communicate bi-directionally, for example, withone or more machines 110 (the machines 110 including one or moresensors, as described herein) at an industrial facility and with one ormore wireless devices (e.g., a UE 120 belonging to a remote engineer,etc.) via one or more antennas, wired, or wireless links. For example,the transceiver 1520 may be or include a wireless transceiver and maycommunicate bidirectionally with another wireless transceiver. In someexamples, the transceiver 1520 may include a modem to modulate datapackets to provide the modulated data packets to the antennas fortransmission and to demodulate packets received from the antennas.

In some examples, the antenna 1525 of the device 1505 may be or includea single antenna 1525. In some other examples, however, the device mayinclude more than one antenna 1525, which may be capable of concurrentlytransmitting and receiving multiple wireless transmissions.

The memory 1530 may include RAM and ROM. The memory 1530 may storecomputer-readable, computer-executable software 1535 includinginstructions that, when executed, cause the processor 1540 to performvarious functions described herein. In some examples, the memory 1530may include, among other things, a basic input/output system (BIOS),which may control basic hardware or software operation such as theinteraction with peripheral components or devices.

The processor 1540 may include one or more intelligent hardware devices(e.g., a general-purpose processor, a digital signal processor (DSP), acentral processing unit (CPU), a microcontroller, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), a programmable logic device (PLD), a discrete gate ortransistor logic component, a discrete hardware component, or anycombination thereof). In some examples, the processor 1540 may beconfigured to operate a memory array using a memory controller. In othercases, the memory controller may be integrated into the processor 1540.The processor 1540 may be configured to execute computer-readableinstructions stored in the memory 1530 to perform various functions(e.g., functions or tasks supporting enhanced reliability techniques forshared spectrum).

FIG. 16 shows a flowchart illustrating a method 1600 that supportsanalyzing machine performance in accordance with aspects of the presentdisclosure. The operations of the method 1600 may be implemented by adevice, such as a server or other computing device, or its components.For example, the operations of the method 1600 may be performed by avibration performance manager 1510 and its components, as described withreference to FIG. 15. In some examples, the device may execute a set ofinstructions to control the functional elements of the device to performthe functions described below. Additionally or alternatively, the devicemay perform aspects of the functions described below usingspecial-purpose hardware.

At 1605, the device may receive vibration data points from one or moresensors associated with one or more machines. The operations of 1605 maybe performed according to the methods described herein. In someexamples, aspects of the operations of 1605 may be performed by avibration data module 1550 as described with reference to FIG. 15.

At 1610, the device may group the vibration data points into a firstgroup of vibration data and a second group of vibration data, where thefirst group of vibration data includes a first set of the vibration datapoints associated with a short-term window and the second group ofvibration data includes a second set of the vibration data pointsassociated with a long-term window. In some examples, each of the firstgroup of vibration data and the second group of vibration data maycorrespond to one of a plurality of time intervals (e.g., one hour timeintervals). The operations of 1610 may be performed according to themethods described herein. In some examples, aspects of the operations of1610 may be performed by a vibration data module as described withreference to FIG. 15.

At 1615, the device may optionally associate a plurality of indicatorswith each of the vibration data points, each indicator of the pluralityof indicators being common to at least a subset of the one or moresensors, where detecting one or more outliers is based on one or more ofthe plurality of indicators, respectively, for the first group ofvibration data, the second group of vibration data, or both. Theoperations of 1615 may be performed according to the methods describedherein. In some examples, aspects of the operations of 1615 may beperformed by a vibration data module as described with reference to FIG.15.

At 1620, the device may detect one or more outliers from the first setof vibration data points and from the second set of vibration datapoints. The operations of 1620 may be performed according to the methodsdescribed herein. In some examples, aspects of the operations of 1620may be performed by an outlier module as described with reference toFIG. 15.

In some examples, at 1625, the device may, as part of the detecting oneor more outliers, optionally apply an isolation algorithm to the firstset of vibration data points of the first group of vibration data and tothe second set of vibration data points of the second group of vibrationdata. The operations of 1625 may be performed according to the methodsdescribed herein. In some examples, aspects of the operations of 1625may be performed by an outlier module as described with reference toFIG. 15.

At 1630, the device may determine a first subset of the one or moreoutliers based on an amplitude of each outlier of the first subsetsatisfying a first threshold. The operations of 1630 may be performedaccording to the methods described herein. In some examples, aspects ofthe operations of 1630 may be performed by an outlier module asdescribed with reference to FIG. 15.

At 1635, the device may determine a second subset of the one or moreoutliers based on an amplitude of each outlier of the second subsetsatisfying a second threshold. The operations of 1635 may be performedaccording to the methods described herein. In some examples, aspects ofthe operations of 1635 may be performed by an outlier module asdescribed with reference to FIG. 15.

At 1640, the device may determine that the vibration data points includean anomaly associated with one or more respective sensors of the one ormore sensors based on a quantity of outliers of the first subset thatare within a detection window relative to a quantity of outliers of thesecond subset that are within the detection window. The operations of1640 may be performed according to the methods described herein. In someexamples, aspects of the operations of 1640 may be performed by ananomaly module as described with reference to FIG. 15.

At 1645, the device may transmit an indication of the one or morerespective sensors associated with the anomaly. The operations of 1645may be performed according to the methods described herein. In someexamples, aspects of the operations of 1645 may be performed by ananomaly module as described with reference to FIG. 15.

These and other aspects, features, and benefits of the claimedinvention(s) are apparent from the following detailed writtendescription of the preferred embodiments and aspects taken inconjunction with the following drawings, although variations andmodifications thereto may be effected without departing from the spiritand scope of the novel concepts of the disclosure.

What is claimed is:
 1. A method for analyzing machine performance,comprising: receiving vibration data points from one or more sensorsassociated with one or more machines; grouping the vibration data pointsinto a first group of vibration data points and a second group ofvibration data points, wherein the first group of vibration datacomprises a first set of the vibration data points associated with ashort-term window and the second group of vibration data comprises asecond set of the vibration data points associated with a long-termwindow, each of the first group of vibration data and the second groupof vibration data corresponding to one of a plurality of time intervals;detecting one or more outliers from the first set of vibration datapoints and from the second set of vibration data points; determining afirst subset of the one or more outliers based on an amplitude of eachoutlier of the first subset satisfying a first threshold; determining asecond subset of the one or more outliers based on an amplitude of eachoutlier of the second subset satisfying a second threshold; determiningthat the vibration data points comprise an anomaly associated with oneor more respective sensors of the one or more sensors based on aquantity of outliers of the first subset that are within a detectionwindow relative to a quantity of outliers of the second subset that arewithin the detection window; and transmitting an indication of the oneor more respective sensors associated with the anomaly.
 2. The method ofclaim 1, further comprising: associating a plurality of indicators witheach of the vibration data points, each indicator of the plurality ofindicators being common to at least a subset of the one or more sensors,wherein detecting the one or more outliers is based on one or more ofthe plurality of indicators, respectively, for the first group ofvibration data, the second group of vibration data, or both.
 3. Themethod of claim 2, wherein the plurality of indicators comprises one ormore of: a sensor type, a corresponding machine, a correspondingfacility, an indicator type, a date-time, a sensor measurement, or acombination thereof.
 4. The method of claim 1, wherein the vibrationdata points comprise one or more of: a peak velocity value, a peakacceleration value, an overall root mean square value, an overallvibration value, or a combination thereof.
 5. The method of claim 1,wherein grouping the vibration data points comprises: normalizing thevibration data of the first group of vibration data and the second groupof vibration data.
 6. The method of claim 1, wherein each time intervalof the plurality of time intervals corresponds to a respective hourinterval.
 7. The method of claim 1, further comprising: modifying thefirst set of vibration data points, the second set of vibration datapoints, or both, based on one or more performance parameters, whereingrouping the vibration data points comprises filtering the vibrationdata points based on the modified data points.
 8. The method of claim 7,wherein the one or more performance parameters comprise one or more of:a processed sensor measurement, a date-time indicator, an indicatortype, a window size parameter, a number of processors, a sensitivityparameter, or a combination thereof.
 9. The method of claim 1, whereindetecting the one or more outliers comprises: applying an isolationalgorithm to the first set of vibration data points of the first groupof vibration data and to the second set of vibration data points of thesecond group of vibration data.
 10. The method of claim 1, whereindetermining that the vibration data points comprise the anomaly is basedon a variation between the quantity of outliers of the first subset thatare within the detection window and the quantity of outliers of thesecond subset that are within the detection window.
 11. The method ofclaim 1, wherein the first threshold is set according to a thresholdpercentile for the first set of vibration data points of the short-termwindow and the second threshold is set according to the thresholdpercentile for the second set of vibration data points of the long-termwindow.
 12. The method of claim 1, further comprising: collecting thevibration data points at discrete intervals.
 13. The method of claim 1,further comprising: associating the vibration data points withrespective ones of the one or more sensors based on a key associatedwith each of the vibration data points.
 14. The method of claim 13,wherein the key comprises an indication of a location of a sensor, asensor function, or both.
 15. The method of claim 1, further comprising:filtering a list of the one or more machines based on determining thatthe anomaly has not occurred at one or more respective sensorsassociated with the one or more machines, wherein the indication of theone or more respective sensors associated with the anomaly is based onthe filtered list.
 16. An apparatus for analyzing machine performance,comprising: a processor; memory coupled with the processor; andinstructions stored in the memory and executable by the processor tocause the apparatus to: receive vibration data points from one or moresensors associated with one or more machines; group the vibration datapoints into a first group of vibration data points and a second group ofvibration data points, wherein the first group of vibration datacomprises a first set of the vibration data points associated with ashort-term window and the second group of vibration data comprises asecond set of the vibration data points associated with a long-termwindow, each of the first group of vibration data and the second groupof vibration data corresponding to one of a plurality of time intervals;detect one or more outliers from the first set of vibration data pointsand from the second set of vibration data points; determine a firstsubset of the one or more outliers based on an amplitude of each outlierof the first subset satisfying a first threshold; determine a secondsubset of the one or more outliers based on an amplitude of each outlierof the second subset satisfying a second threshold; determine that thevibration data points comprise an anomaly associated with one or morerespective sensors of the one or more sensors based on a quantity ofoutliers of the first subset that are within a detection window relativeto a quantity of outliers of the second subset that are within thedetection window; and transmit an indication of the one or morerespective sensors associated with the anomaly.
 17. The apparatus ofclaim 16, wherein the instructions are further executable by theprocessor to cause the apparatus to: associate a plurality of indicatorswith each of the vibration data points, each indicator of the pluralityof indicators being common to at least a subset of the one or moresensors, wherein detecting the one or more outliers is based on one ormore of the plurality of indicators, respectively, for the first groupof vibration data, the second group of vibration data, or both.
 18. Theapparatus of claim 17, wherein the plurality of indicators comprises oneor more of: a sensor type, a corresponding machine, a correspondingfacility, an indicator type, a date-time, a sensor measurement, or acombination thereof.
 19. The apparatus of claim 16, wherein thevibration data points comprise one or more of: a peak velocity value, apeak acceleration value, an overall root mean square value, an overallvibration value, or a combination thereof.
 20. The apparatus of claim16, wherein the instructions to group the vibration data points arefurther executable by the processor to cause the apparatus to: normalizethe vibration data of the first group of vibration data and the secondgroup of vibration data.
 21. The apparatus of claim 16, wherein eachtime interval of the plurality of time intervals corresponds to arespective hour interval.
 22. The apparatus of claim 16, wherein theinstructions are further executable by the processor to cause theapparatus to: modify the first set of vibration data points, the secondset of vibration data points, or both, based on one or more performanceparameters, wherein grouping the vibration data points comprisesfiltering the vibration data points based on the modified data points.23. The apparatus of claim 22, wherein the one or more performanceparameters comprise one or more of: a processed sensor measurement, adate-time indicator, an indicator type, a window size parameter, anumber of processors, a sensitivity parameter, or a combination thereof.24. The apparatus of claim 16, wherein the instructions to detect theone or more outliers are further executable by the processor to causethe apparatus to: apply an isolation algorithm to the first set ofvibration data points of the first group of vibration data and to thesecond set of vibration data points of the second group of vibrationdata.
 25. The apparatus of claim 16, wherein the instructions todetermine that the vibration data points comprise the anomaly are basedon a variation between the quantity of outliers of the first subset thatare within the detection window and the quantity of outliers of thesecond subset that are within the detection window.
 26. The apparatus ofclaim 16, wherein the first threshold is set according to a thresholdpercentile for the first set of vibration data points of the short-termwindow and the second threshold is set according to the thresholdpercentile for the second set of vibration data points of the long-termwindow.
 27. The apparatus of claim 16, wherein the instructions arefurther executable by the processor to cause the apparatus to: collectthe vibration data points at discrete intervals.
 28. The apparatus ofclaim 16, wherein the instructions are further executable by theprocessor to cause the apparatus to: associate the vibration data pointswith respective ones of the one or more sensors based on a keyassociated with each of the vibration data points.
 29. The apparatus ofclaim 28, wherein the key comprises an indication of a location of asensor, a sensor function, or both.
 30. The apparatus of claim 16,wherein the instructions are further executable by the processor tocause the apparatus to: filter a list of the one or more machines basedon determining that the anomaly has not occurred at one or morerespective sensors associated with the one or more machines, wherein theindication of the one or more respective sensors associated with theanomaly is based on the filtered list.